How to Merge/Join Two Pandas DataFrames?

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To merge or join two Pandas DataFrames, you can use the merge() function provided by Pandas. This function allows you to combine DataFrames based on a common column or key. Here is an explanation of how to perform this operation:

  1. Import the necessary libraries:
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import pandas as pd


  1. Create the DataFrames that you want to merge, for example:
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df1 = pd.DataFrame({'ID': [1, 2, 3],
                    'Name': ['John', 'Alice', 'Bob']})

df2 = pd.DataFrame({'ID': [2, 3, 4],
                    'Age': [25, 30, 35]})


  1. Choose the type of merge you want to perform. Common merge types include: Inner merge: Retains only the common rows between both DataFrames. Left merge: Retains all rows from the left DataFrame and fills missing values with NaN for the right DataFrame. Right merge: Retains all rows from the right DataFrame and fills missing values with NaN for the left DataFrame. Outer merge: Retains all rows from both DataFrames and fills missing values with NaN.
  2. Merge the DataFrames using the merge() function:
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merged_df = pd.merge(df1, df2, on='ID', how='inner')


In this example, the 'ID' column is used as the common key for merging, and an inner merge is performed. This results in a new DataFrame called merged_df.

  1. Check the merged result:
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print(merged_df)


The output will be:

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   ID   Name  Age
0   2  Alice   25
1   3    Bob   30


The resulting DataFrame contains only the common rows from both DataFrames based on the 'ID' column.


By following these steps, you will be able to merge or join two Pandas DataFrames using the merge() function.

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How to merge/join DataFrames while handling duplicate column names?

When merging or joining DataFrames, it is possible to encounter duplicate column names. This situation can be handled using the suffixes parameter of pandas merge or join functions. Here's an example of how to merge DataFrames while preserving and distinguishing duplicate column names:

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import pandas as pd

# Create two example DataFrames
df1 = pd.DataFrame({'ID': [1, 2, 3], 'Value': ['A', 'B', 'C']})
df2 = pd.DataFrame({'ID': [1, 2, 3], 'Value': ['X', 'Y', 'Z']})

# Merge the DataFrames with duplicate column names
df_merged = df1.merge(df2, on='ID', suffixes=('_left', '_right'))

# Output the merged DataFrame
print(df_merged)


Output:

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   ID Value_left Value_right
0   1          A           X
1   2          B           Y
2   3          C           Z


In this example, df_merged is the result of merging df1 and df2 using the common column 'ID'. The suffixes parameter is used to append custom suffixes to the column names from the left and right DataFrames. This way, the resulting merged DataFrame retains and differentiates the duplicate column names.


What is the default join type in Pandas DataFrame merge/join?

The default join type in Pandas DataFrame merge/join is an "inner" join.


What is an inner join and when is it appropriate to use during DataFrames merging/joining?

An inner join is a type of join operation that returns only the records that have matching values in both DataFrames being merged. It merges the two DataFrames based on a common key or column.


An inner join is appropriate to use when we want to combine the records from two DataFrames that have matching values in the specified key or column. It filters out the non-matching records, ensuring that only the common records are included in the result.


For example, consider two DataFrames: DataFrame A and DataFrame B. If we perform an inner join between these DataFrames using a common key, the output DataFrame will only contain the records where the key values are present in both DataFrame A and DataFrame B.


Inner join is useful when we want to combine two DataFrames based on shared values and exclude the non-matching records. It helps in consolidating and aggregating data from multiple sources or tables where the common key serves as the linking factor.

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